code: def get_variable(
      value: Any,
      framework: str = "tf",
      trainable: bool = False,
      tf_name: str = "unnamed-variable",
      torch_tensor: bool = False,
      device: Optional[str] = None,
      shape: Optional[TensorShape] = None,
      dtype: Optional[TensorType] = None,
  ) -> Any:
      """Creates a tf variable, a torch tensor, or a python primitive.
  
      Args:
          value: The initial value to use. In the non-tf case, this will
              be returned as is. In the tf case, this could be a tf-Initializer
              object.
          framework: One of "tf", "torch", or None.
          trainable: Whether the generated variable should be
              trainable (tf)/require_grad (torch) or not (default: False).
          tf_name: For framework="tf": An optional name for the
              tf.Variable.
          torch_tensor: For framework="torch": Whether to actually create
              a torch.tensor, or just a python value (default).
          device: An optional torch device to use for
              the created torch tensor.
          shape: An optional shape to use iff `value`
              does not have any (e.g. if it's an initializer w/o explicit value).
          dtype: An optional dtype to use iff `value` does
              not have any (e.g. if it's an initializer w/o explicit value).
              This should always be a numpy dtype (e.g. np.float32, np.int64).
  
      Returns:
          A framework-specific variable (tf.Variable, torch.tensor, or
          python primitive).
      """
      if framework in ["tf2", "tf"]:
          import tensorflow as tf
  
          dtype = dtype or getattr(
              value,
              "dtype",
              tf.float32
              if isinstance(value, float)
              else tf.int32
              if isinstance(value, int)
              else None,
          )
          return tf.compat.v1.get_variable(
              tf_name,
              initializer=value,
              dtype=dtype,
              trainable=trainable,
              **({} if shape is None else {"shape": shape}),
          )
      elif framework == "torch" and torch_tensor is True:
          torch, _ = try_import_torch()
          if not isinstance(value, np.ndarray):
              value = np.array(value)
          var_ = torch.from_numpy(value)
          if dtype in [torch.float32, np.float32]:
              var_ = var_.float()
          elif dtype in [torch.int32, np.int32]:
              var_ = var_.int()
          elif dtype in [torch.float64, np.float64]:
              var_ = var_.double()
  
          if device:
              var_ = var_.to(device)
          var_.requires_grad = trainable
          return var_
      # torch or None: Return python primitive.
      return value
  
imports: import logging
  import numpy
  import os
  import sys
  from typing import Any, Optional
  from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.typing import TensorShape, TensorType
label_desc: Use image node_id 7 for calling a global function with example usage: get_variable(value, framework, trainable, tf_name, torch_tensor, device, shape, dtype) and returns: value, tf, var_